{"title":"图像统计如何影响有损编码性能?","authors":"S. Saha, V. Vemuri","doi":"10.1109/ITCC.2000.844181","DOIUrl":null,"url":null,"abstract":"It has been observed (Saha and Vemuri, 1999) that when we compress a variety of images of different types using a fixed wavelet filter, the peak signal-to-noise ratio (PSNR) values vary widely from image to image. This large variation in PSNR by as much as 30 dB, can only be attributed to the nature and inherent characteristics of the image, since everything else is fixed. In this paper, we analyze the set of test images to determine the features in the images that may cause the coding performance variations. It is shown that most of the gray-level image features do not have any direct effect on the coding performance, and image activity measure is the only feature that has a correlation with the PSNR value.","PeriodicalId":146581,"journal":{"name":"Proceedings International Conference on Information Technology: Coding and Computing (Cat. No.PR00540)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"How do image statistics impact lossy coding performance?\",\"authors\":\"S. Saha, V. Vemuri\",\"doi\":\"10.1109/ITCC.2000.844181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It has been observed (Saha and Vemuri, 1999) that when we compress a variety of images of different types using a fixed wavelet filter, the peak signal-to-noise ratio (PSNR) values vary widely from image to image. This large variation in PSNR by as much as 30 dB, can only be attributed to the nature and inherent characteristics of the image, since everything else is fixed. In this paper, we analyze the set of test images to determine the features in the images that may cause the coding performance variations. It is shown that most of the gray-level image features do not have any direct effect on the coding performance, and image activity measure is the only feature that has a correlation with the PSNR value.\",\"PeriodicalId\":146581,\"journal\":{\"name\":\"Proceedings International Conference on Information Technology: Coding and Computing (Cat. No.PR00540)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings International Conference on Information Technology: Coding and Computing (Cat. No.PR00540)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITCC.2000.844181\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings International Conference on Information Technology: Coding and Computing (Cat. No.PR00540)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITCC.2000.844181","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How do image statistics impact lossy coding performance?
It has been observed (Saha and Vemuri, 1999) that when we compress a variety of images of different types using a fixed wavelet filter, the peak signal-to-noise ratio (PSNR) values vary widely from image to image. This large variation in PSNR by as much as 30 dB, can only be attributed to the nature and inherent characteristics of the image, since everything else is fixed. In this paper, we analyze the set of test images to determine the features in the images that may cause the coding performance variations. It is shown that most of the gray-level image features do not have any direct effect on the coding performance, and image activity measure is the only feature that has a correlation with the PSNR value.
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